Adi Polak
Data Streaming & AI/ML @ Confluent | Author of Scaling Machine Learning Systems (O'Reilly)
Adi Polak is an experienced software engineer and people manager. For most of her professional life, she dealt with data and machine learning for operations and analytics. As a data practitioner, she developed algorithms to solve real-world problems using machine learning techniques and leveraging expertise in Apache Spark, Kafka, HDFS, and distributed large-scale systems. As a manager, she led teams, and together they embarked on innovative journeys in the ML space and came back with staggering insights and learnings. Adi has taught Spark to thousands of students and is the author of the successful book Scaling Machine Learning with Spark. Earlier this year, she began a new adventure with data streaming, specifically Flink and ML inference, and is hooked.
Sessions
Navigating Streaming Infrastructure
When designing a distributed system architecture there will always be contradictory requirements. Sometimes we'll be referred to as constraints. Like the CAP theorem, where developers designing a system have to choose between what is physically possible between consistency, availability and partition tolerance. The same applies to streaming infrastructure systems. Optimizing for one will interfere with optimizing for the other. When it comes to streaming infrastructure, cost, throughput, accuracy, and latency are the main constraints. What latency should be? How much are we willing to pay for the infrastructure that justifies the business costs? Are we ok with late arriving data? What about losing data? How accurate should it be? And so on. During this session, you will learn how various decisions in your system design affect your overall system capabilities, how Flink Streaming and Spark Streaming differ in their approach, both conceptually and design, and how composition of multiple solutions can be beneficial to you, the team and the business.
Starts: 9:55 AM
Ends: 10:40 AM
Future of Machine Learning Panel
Our "Future of Machine Learning" panel will explore cutting-edge developments and emerging trends that are shaping the field. Leading experts will discuss advancements in areas such as deep learning architectures, reinforcement learning, and federated learning. The panel will delve into practical applications of ML. Join us for a fascinating look into the future of machine learning and its potential to revolutionize industries, scientific research, and our daily lives.
Starts: 1:40 PM
Ends: 2:30 PM